Overview

This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. You will learn to build advanced AI agents using AWS Bedrock, a platform for creating generative AI applications. The course introduces key concepts like Retrieval-Augmented Generation (RAG) and function orchestration to enhance AI models with external data. As you progress, you'll gain hands-on experience deploying chatbots, creating knowledge bases, and integrating AWS services like Lambda and DynamoDB.. Throughout the course, you’ll dive deeper into working with multi-agent systems and their applications in real-world scenarios like product inventory management and mortgage processing. By the end, you'll have the skills to build fully functional, scalable AI agents that can interact with complex data sources. This course is perfect for developers with some Python and cloud experience. Knowledge of basic cloud computing concepts is helpful, but no prior experience with AWS Bedrock is required. Ideal for those aiming to create sophisticated AI solutions.

Syllabus

Course Introduction & Background

In this module, we will introduce the foundational concepts of the course and provide an overview of the key industry roles emerging due to AI advancements. We will also review essential prerequisites to ensure you are well-prepared to proceed through the course successfully.

Fundamental Concepts in LLM-Driven Apps & AI

In this module, we will explore key concepts in AI app development, focusing on RAG architecture and its importance. Additionally, you will learn how function calling and orchestration enhance workflows, as well as gain an understanding of agentic AI and its ability to enable task automation.

Dependent Software Installation

In this module, we will guide you through the installation of the necessary tools and frameworks, including Docker and AWS CLI. This ensures that your development environment is properly configured for engaging in practical labs throughout the course.

Introduction to AWS Bedrock

In this module, we will introduce you to AWS Bedrock, a powerful platform for building generative AI applications. You'll learn to navigate the console, interact with services via Python/Boto3, and deploy AI applications such as a chatbot on AWS ECS.

Working with Bedrock KnowledgeBase as Vector Store

In this module, we will cover how to utilize the Bedrock KnowledgeBase as a vector store to enhance AI model responses. You’ll learn to build and manage a KnowledgeBase and integrate it with Lambda functions and Python SDK to interact with AI applications.

Getting Started with Bedrock Agents

In this module, we will introduce Bedrock agents and guide you through setting up essential data sources for these agents. You’ll learn to deploy and manage agents, giving you the tools to create intelligent systems capable of executing tasks autonomously.

Introduction to Multi-Agent Collaboration Using Bedrock

In this module, we will dive into the concept of multi-agent collaboration, where agents work together to solve complex tasks. You will set up multiple agents and configure them for tasks like mortgage assistance, culminating in deploying a chatbot to AWS ECS.

Lab - Develop Hotel Booking Assistant with AWS Bedrock, Dynamo & Lambda Functions

In this module, we will guide you through the development of a hotel booking assistant, integrating AWS Bedrock with Lambda functions and DynamoDB. You will complete a full deployment to AWS ECS for performance and scalability.

Redshift as a KnowledgeBase for Structured Data

In this module, we will explore using Amazon Redshift as a structured data source to enhance the Bedrock KnowledgeBase. You'll learn how to set up Redshift, integrate it with Bedrock, and query it using Lambda functions to support AI agents.

Workflows for Generative AI Apps Using Bedrock Flows

In this module, we will introduce you to Bedrock Flows, simplifying the creation of workflows for AI applications. You will build and deploy workflows for use cases like eCommerce feedback, ensuring efficient and reliable AI responses integrated with Bedrock KnowledgeBase.